Quantum computers are notoriously prone to errors due to decoherence and other quantum effects. Deep learning models can learn to detect and
Quantum computing is viewed in many ways as the successor of classical computers — subsequently, quantum machine learning would be the successor of classical machine learning models. The theory of quantum machine learning is derived from the various concepts of quantum computing, machine learning, probabilistic theories, and classical ML models. While improving the run times of machine learning models using quantum computing will certainly boost efficiency, there are other ways to do so–such as the fact that QML models have the potential to learn from smaller amounts of data. So from a practical standpoint, quantum computing machine learning models can efficiently factor and classify complex yet condensed data sets. Quantum machine learning models can run through far more permutations and analyze the data yielded from each interaction. In the long term, the increased learning capacity and efficiency of quantum machine learning models may prove useful for solving some of the world’s greatest challenges.
Efficient encoding ensures that Quantum Machine Learning models can process large datasets effectively while maintaining computational feasibility within the
Better Machine-Learning Models With Quantum Computers. ## Create an account to access more content and features on *IEEE Spectrum* , including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. ## Join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of *IEEE Spectrum’s* articles, archives, PDF downloads, and other benefits. # Better Machine-Learning Models With Quantum Computers. Researchers at the quantum-computing company Terra Quantum have demonstrated improved training of machine-learning models by using a new method that combines the best features of classical and quantum computers. Classical and quantum computers can both be used to train machine-learning models, which essentially means solving equations in high-dimensional spaces. A key insight from the research is that by giving classical and quantum computers the same dataset and allowing them to train models in parallel, the final model, consisting of a combination of the two, could achieve better results, says Melnikov, a coauthor on the research paper.
Quantum properties like superposition and entanglement could accelerate machine learning by handling vast, high-dimensional data more
Quantum computing offers the necessary computational horsepower to speed up complex machine learning algorithms, and machine learning provides a
Home / Chroniques / Quantum computing and AI: less compatible than expected? # Quantum computing and AI: less compatible than expected? Assistant Professor of AI and Quantum Physics at Ecole Polytechnique (IP Paris). * There is a belief that quantum computing could revolutionise artificial intelligence and in particular deep learning. * However, quantum computing will not necessarily advance AI because it encounters difficulties in processing information from neural networks and voluminous data. * However, AI machine learning is an essential tool for learning how to design and operate quantum computers today. ## What can be said about the origins behind the belief that quantum computing could revolutionise AI? The idea that quantum computing could boost AI development became more prominent around 2018–19. But every time you run a quantum algorithm the output will be different. ## Does that mean that AI and quantum computing will be distant cousins, with little overlap? AI could also be very complimentary to quantum computing. 2[https://www.openpetition.eu/petition/online/support-the-machine-learning-in-quantum-science-manifesto‑2↑](#note-content-2).
This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks.